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Vibration sensor based tool condition monitoring using ν support vector machine and locality preserving projection

SENSORS AND ACTUATORS A-PHYSICAL 2014年第0期209卷 24-32页

作者： Wang, G. F. Yang, Y. W. Zhang, Y. C. Xie, Q. L. Tianjin Univ Tianjin Key Lab Equipment Design & Mfg Technol Tianjin 300072 Peoples R China

Reliable online monitoring of the tool condition is paramount for automatic machining process. C-support vector machine (C-SVM) has got many successful applications in the field of tool wear monitoring. However, the selection of penalty parameter C is usually realized based on optimization process, which increases the training time of the classifier greatly. In this paper, nu support vector machine (nu-SVM) is presented to realize multi categories tool wear classification. In this model, C is replaced by a new parameter nu which represents an upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. At the same time, the nearest neighbor (NN) based rule is proposed to realize the fast selection of v based on training samples. In addition, to further improve training speed and classification accuracy, locality preserving projection (LPP) method is utilized to reduce the dimension of feature vectors by extracting the lower dimensional manifold characteristics. To testify the effectiveness of the proposed method, milling experiment of Ti6Al4V alloy was carried out and vibration signals corresponding to four kinds of tool wear status were collected. Time domain and frequency domain features are extracted based on wavelet packet decomposition and dimension reduction is realized by using LPP algorithm. Based on the selected features, both C-SVM and nu-SVM are utilized to realize the classification of multi categories tool wear status. The analysis shows that the combination of NN based nu-SVM with LPP can realize faster training of classifier without sacrificing the classification accuracy. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.

关键词： nu-SVM Tool condition monitoring Dimension reduction nearest neighbor algorithm Locality preserving projection

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Large-Scale Methodological Comparison of Acute Hypotensive Episode Forecasting Using MIMIC2 Physiological Waveforms

Large-Scale Methodological Comparison of Acute Hypotensive E...

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27th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

作者： Kim, Yongwook Bryce Seo, Joohyun O'Reilly, Una-May MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA MIT Microsyst Technol Labs Cambridge MA 02139 USA

ISBN: (纸本)9781479944354

We compare the dynamic Bayesian network and k-nearest neighbor-based predictors for the occurrence of acute hypotensive episodes (AHE) with respect to various data conditions (size, class balance ratio) and problem definition settings (lag, lead time). From our dataset extracted from the large ICU physiological waveform repository of MIMIC2 database, we find that both models are effective for predicting AHE and their performances improve with increasing training dataset size. We also empirically demonstrate that the nearest neighbor method has a better performance for larger datasets in terms of both prediction result and computational time, but it severely degrades for class imbalanced data while the Bayesian network remains robust.

关键词： Data models Hidden Markov models Physiology Sensitivity Time series analysis Training Training data data models Training data Hidden Markov models Bayesian networks time series analysis Physiology Functional training nearest neighbor algorithm hypotensive episode Dataset waveforms Training LEAD TIME

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A high performance systolic architecture for k-NN classification 14

A high performance systolic architecture for k-NN classifica...

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12th ACM/IEEE International Conference on Methods and Models for System Design, MEMOCODE 2014

作者： Townsend, Kevin R. Jones, Phillip Zambreno, Joseph Department of Electrical and Computer Engineering Iowa State University AmesIA United States

ISBN: (纸本)9781479953387

This paper describes the architecture of the winning entry to the 2014 Memocode Design Contest, in the maximum performance category. This year's Memocode design contest asks contestants to find the 10 nearest neighbors between 1,000 testing points and 10,000,000 training points. Instead of using Euclidean distance, the contest uses Mahalanobis distance. The contest has 2 awards: the maximum performance award and the cost adjusted performance award. Our implementation uses a brute force approach that calculates the distance between every testing point to every training point. We use the Convey HC-2ex, a FPGA-based platform. However, the theory applies to software implementations as well. At the time of publication, our runtime is 0.54 seconds. © 2014 IEEE.

关键词： field programmable gate arrays parallel processing pattern classification software architecture Euclidean distance FPGA-based platform Mahalanobis distance Memocode design high performance systolic architecture k-NN classification maximum performance category software implementations Clocks Field programmable gate arrays Random access memory Runtime Table lookup Training Vectors Field programmable gate arrays Runtime Table lookup Euclidean distance random access machine Timing devices Pattern recognition Software architecture PARALLEL PROCESSING (COMPUTERS) Functional training software implementation nearest neighbor Awards nearest neighbor algorithm

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Research on Case-based Reasoning Search Method

Research on Case-based Reasoning Search Method

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26th Chinese Control and Decision Conference (CCDC)

作者： Li, Hongru Zhao, Chaoyue Wu, Wei Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781479937066

The search method based on the way combined self-organizing competitive neural network with nearest neighbor algorithm was proposed. A data preprocessing method applying to the retrieval method was proposed in this paper. The implementation process of the search method based on the way combined self-organizing competitive neural network with nearest neighbor was showed. Eventually, through a practical example, the feasibility and efficiency of combination of self-organizing competitive neural network and nearest neighbor method were demonstrated.

关键词： CBR Self-organizing Competitive Neural Network nearest neighbor algorithm Hierarchical Retrieval

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Sampling-based Trajectory Imitation in Constrained Environments using Laplacian-RRT

Sampling-based Trajectory Imitation in Constrained Environme...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Thomas Nierhoff Sandra Hirche Yoshihiko Nakamura Institute of Automatic Control Engineering (LSR) Faculty of Electrical Engineering Technische Universitat Munchen D-80290 Munchen Germany Department of Mechano-Informatics University of Tokyo 7-3-1 Hongo Bunkyo-ku 113-8656 Tokyo Japan

ISBN: (纸本)9781479969357

This paper presents an incremental sampling-based approach for trajectory imitation in cluttered environments using the RRT~* algorithm. Inspired by the discrete Laplace-Beltrami operator the underlying distance metric is based upon the difference from a reference trajectory through a quadratic distance term incorporating velocity and acceleration deviations along the trajectory. Mathematically-backed approximations in combination with a task-space bias make it possible to use standard nearest neighbor methods in task space when expanding the RRT~*-tree. It is shown that metric-consistent biases considerably increase the convergence speed. The proposed approach is validated in simulations in a 2D environment and in experiments using a HRP-4 humanoid robot.

关键词： trajectory imitation Task space distance metric Humanoid robots Environments nearest neighbor algorithm Converge

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Prediction of Metabolic Pathway Using Graph Property, Chemical Functional Group and Chemical Structural Set

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CURRENT BIOINFORMATICS 2013年第2期8卷 200-207页

作者： Chen, Lei Zeng, Wei-Ming Cai, Yu-Dong Huang, Tao Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Shanghai Univ Inst Syst Biol Shanghai 200444 Peoples R China Fudan Univ Ctr Computat Syst Biol Shanghai 200433 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Key Lab Syst Biol Shanghai 200031 Peoples R China Shanghai Ctr Bioinformat Technol Shanghai 200235 Peoples R China

In systems biology, it is a great challenge for researchers to identify whether the given set of organic compounds can combine together and form a meaningful pathway. Fortunately, it becomes more and more feasible to address and solve such a problem with the rapidly accumulated information on various organisms. Based on the attainable information, a novel computational approach is proposed to investigate this problem by adopting the metabolic pathway of yeast as the subject of the study. And we produced a benchmark dataset with 13,736 pathways consisting of both valid and invalid pathways and identified the valid pathways among them. Each of these pathways was encoded into a numeric vector, consisting of three parts: graph property, chemical functional group, and chemical structural set. Methods of Minimum Redundancy Maximum Relevance and Incremental Feature Selection were utilized to select an optimal feature set, and nearest neighbor algorithm was adopted as the classification model, while Jackknife Test was used to evaluate the model. As a result, an optimal feature set consisting of 16 features, which were able to identify the valid pathways most successfully, was obtained.

关键词： Chemical functional group chemical structural set compound similarity metabolic pathway minimum redundancy maximum relevance nearest neighbor algorithm jackknife cross-validation

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Predicting Biological Functions of Protein Complexes Using Graphic and Functional Features

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CURRENT BIOINFORMATICS 2013年第5期8卷 545-551页

作者： Chen, Lei Li, Bi-Qing Feng, Kai-Yan Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Key Lab Syst Biol Shanghai 200031 Peoples R China Beijing Genom Inst Shenzhen 518083 Peoples R China

Protein complexes involve in most if not all of essential biological processes in a living cell. Many attempts have been devoted to identify protein complexes using computational methods, most of which exploit protein-protein interaction networks to search intensively interacting proteins as a protein complex. Besides identifying protein complexes, knowing their biological functions may help unlock their molecular mechanisms and their roles in related biological processes. Therefore, it is also desirable to computationally predict the functions of protein complexes. However, no literature has been found to address such a problem. This paper attempts to address the problem by choosing yeast as the model organism, where total 50 protein complexes are collected and their functions are validated by solid experiments. Each of the complexes was encoded by a numeric vector based upon their graphic and functional properties. Feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract core features for the prediction. Three different prediction methods, nearest neighbor algorithm, Bayesian network and Sequential Minimal Optimization, were utilized in this study and tested by jackknife cross-validation test. Consequently, 22 core features coupled with nearest neighbor algorithm gain the highest accuracy. These core features are regarded as the most important features for the determination of the biological functions of protein complexes. 19 out of 22 core features were from functional properties, indicating that the functions of each protein component probably constrain the overall functions of the protein complex.

关键词： Bayesian network gene ontology incremental feature selection jackknife test minimum redundancy maximum relevance nearest neighbor algorithm prediction of functions of protein complex protein complex sequential minimal optimization

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Protein Functional Class Prediction Based on Clustering

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2013年第1期10卷 27-32页

作者： Xiang, Qilin Liao, Bo Liu, Qinfeng Lu, Xinguo Zhu, Wen Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China

Protein functional prediction is becoming a challenge in the post-genomic era. Therefore, it is in high demand to develop an automated method which can predict the protein functional class rapidly and accurately. In this paper, we propose a ProfileAA coding and an ExtendProfile coding, then evaluate and compare the two coding methods. Furthermore we choose the ProfileAA coding, which integrates amino acid composition information with amino acid physical and chemical properties information. Moreover, by comparing the coding with three other coding methods, we find that this coding is more reasonable. Next, we predict the protein functional class based on Shortest Path Clustering, combining with nearest neighbor algorithm (NNA). The experimental result shows that our method is more efficient to predict protein functional class.

关键词： Clustering Method Sequence Coding Protein Functional Class nearest neighbor algorithm

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Prediction of protein amidation sites by feature selection and analysis

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MOLECULAR GENETICS AND GENOMICS 2013年第9期288卷 391-400页

作者： Cui, Weiren Niu, Shen Zheng, Lulu Hu, Lele Huang, Tao Gu, Lei Feng, Kaiyan Zhang, Ning Cai, Yudong Li, Yixue Chinese Acad Sci Shanghai Inst Biol Sci CAS MPG Partner Inst Computat Biol Shanghai 200031 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Key Lab Syst Biol Shanghai 200031 Peoples R China Shanghai Ctr Bioinformat Technol Shanghai 200235 Peoples R China Huazhong Univ Sci & Technol Hubei Bioinformat & Mol Imaging Key Lab Wuhan 430074 Peoples R China Shanghai Univ Inst Syst Biol Shanghai 200444 Peoples R China German Canc Res Ctr Div Theoret Bioinformat BO80 D-69120 Heidelberg Germany Tianjin Univ Dept Biomed Engn Tianjin Key Lab BME Measurement Tianjin 300072 Peoples R China Shanghai Univ Dept Math Coll Sci Shanghai 200444 Peoples R China

Carboxy-terminal alpha-amidation is a widespread post-translational modification of proteins found widely in vertebrates and invertebrates. The alpha-amide group is required for full biological activity, since it may render a peptide more hydrophobic and thus better be able to bind to other proteins, preventing ionization of the C-terminus. However, in particular, the C-terminal amidation is very difficult to detect because experimental methods are often labor-intensive, time-consuming and expensive. Therefore, in silico methods may complement due to their high efficiency. In this study, a computational method was developed to predict protein amidation sites, by incorporating the maximum relevance minimum redundancy method and the incremental feature selection method based on the nearest neighbor algorithm. From a total of 735 features, 41 optimal features were selected and were utilized to construct the final predictor. As a result, the predictor achieved an overall Matthews correlation coefficient of 0.8308. Feature analysis showed that PSSM conservation scores and amino acid factors played the most important roles in the alpha-amidation site prediction. Site-specific feature analyses showed that features derived from the amidation site itself and adjacent sites were most significant. This method presented could be used as an efficient tool to theoretically predict amidated peptides. And the selected features from our study could shed some light on the in-depth understanding of the mechanisms of the amidation modification, providing guidelines for experimental validation.

关键词： Amidation Maximum relevance minimum redundancy Incremental feature selection nearest neighbor algorithm

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Rescaling of Low Frequency DCT Coefficients with Kernel PCA for Illumination Invariant Face Recognition

Rescaling of Low Frequency DCT Coefficients with Kernel PCA ...

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3rd IEEE International Advance Computing Conference (IACC)

作者： Goel, Tripti Nehra, Vijay Vishwakarma, Virendra P. Bhagat Phool Singh Mahila Vishwavidyalaya Khanpur Kalan Haryana India Bhagat Phool Singh Mahila Vishwavidyalaya Sch Sci & Engn Khanpur Kalan Haryana India Jaypee Inst IT Dept CS Noida India Jaypee Inst IT Dept IT Noida India

ISBN: (纸本)9781467345286

Illumination variations significantly affect the performance of the automatic face recognition system. To achieve optimum contrast enhancement, contrast limiting adaptive histogram equalization (CLAHE) has been used in this work. Histogram Equalization (HE) modifies the histogram of the image globally based on intensity distribution of an entire image. However, the feature of interest in an image needs enhancement locally. CLAHE is based on the intensity distribution in a neighborhood of every pixel in the image. Further, for removing the illumination variations in the face image, the appropriate number of low frequency DCT coefficients has been scaled down as illumination variations mainly lie in the low-frequency band. After eliminating illumination variations effect, mapping of the data on to another feature space is done using Kernel PCA (KPCA), which extract higher order statistics. KPCA has the advantage of less computation time and improve performance level. Classification is done by using nearest neighbor classifier. Experiments are performed on Extended Yale B database. The experimental results show that the performance of our method is significantly better than that of any existing state-of-art technique.

关键词： Adaptive Histogram Equalization Discrete Cosine Transform Kernel Principal Component Analysis nearest neighbor algorithm

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